Overview

Dataset statistics

Number of variables36
Number of observations1033
Missing cells1655
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.7 KiB
Average record size in memory288.1 B

Variable types

Categorical9
Boolean14
Numeric13

Alerts

AWM year 1 is highly correlated with Overall AWMHigh correlation
AWM year 2 is highly correlated with Overall AWMHigh correlation
AWM year 3 is highly correlated with Overall AWMHigh correlation
Overall AWM is highly correlated with AWM year 1 and 4 other fieldsHigh correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with Overall AWM and 1 other fieldsHigh correlation
Pass is highly correlated with Overall AWM and 1 other fieldsHigh correlation
English is highly correlated with MathsHigh correlation
Maths is highly correlated with EnglishHigh correlation
AWM year 1 is highly correlated with Overall AWMHigh correlation
AWM year 2 is highly correlated with Overall AWMHigh correlation
AWM year 3 is highly correlated with Overall AWMHigh correlation
Overall AWM is highly correlated with AWM year 1 and 4 other fieldsHigh correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with Overall AWM and 1 other fieldsHigh correlation
Pass is highly correlated with Overall AWM and 1 other fieldsHigh correlation
AWM year 1 is highly correlated with Overall AWMHigh correlation
AWM year 2 is highly correlated with Overall AWMHigh correlation
Overall AWM is highly correlated with AWM year 1 and 1 other fieldsHigh correlation
First Sit is highly correlated with Second SitHigh correlation
Second Sit is highly correlated with First SitHigh correlation
Fails is highly correlated with PassHigh correlation
Pass is highly correlated with FailsHigh correlation
desertion is highly correlated with ProgressHigh correlation
Polar 4 Score is highly correlated with BursaryHigh correlation
Bursary is highly correlated with Polar 4 ScoreHigh correlation
A Levels is highly correlated with BtecHigh correlation
Progress is highly correlated with desertionHigh correlation
British is highly correlated with Student VisaHigh correlation
Btec is highly correlated with A LevelsHigh correlation
Student Visa is highly correlated with BritishHigh correlation
Course is highly correlated with UCAS and 1 other fieldsHigh correlation
UCAS is highly correlated with Course and 1 other fieldsHigh correlation
Disability is highly correlated with BursaryHigh correlation
desertion is highly correlated with UCAS and 9 other fieldsHigh correlation
British is highly correlated with English native Language and 1 other fieldsHigh correlation
English native Language is highly correlated with BritishHigh correlation
Polar 4 Score is highly correlated with BursaryHigh correlation
SLC is highly correlated with Student VisaHigh correlation
Care Leaver is highly correlated with RefugeeHigh correlation
Student Visa is highly correlated with SLCHigh correlation
Refugee is highly correlated with Care LeaverHigh correlation
London Permanent Residence is highly correlated with BritishHigh correlation
UCAS Points is highly correlated with English and 1 other fieldsHigh correlation
English is highly correlated with UCAS Points and 1 other fieldsHigh correlation
Maths is highly correlated with UCAS Points and 1 other fieldsHigh correlation
A Levels is highly correlated with BtecHigh correlation
Btec is highly correlated with A LevelsHigh correlation
Bursary is highly correlated with Disability and 1 other fieldsHigh correlation
Attendance is highly correlated with desertion and 3 other fieldsHigh correlation
AWM year 1 is highly correlated with desertion and 2 other fieldsHigh correlation
AWM year 2 is highly correlated with Course and 5 other fieldsHigh correlation
AWM year 3 is highly correlated with desertion and 6 other fieldsHigh correlation
Overall AWM is highly correlated with desertion and 7 other fieldsHigh correlation
Progress is highly correlated with desertion and 7 other fieldsHigh correlation
First Sit is highly correlated with desertion and 2 other fieldsHigh correlation
Second Sit is highly correlated with First Sit and 1 other fieldsHigh correlation
Fails is highly correlated with desertion and 4 other fieldsHigh correlation
No Submissions is highly correlated with First Sit and 2 other fieldsHigh correlation
Pass is highly correlated with desertion and 4 other fieldsHigh correlation
Re Takes is highly correlated with AWM year 3 and 1 other fieldsHigh correlation
Ethnicity has 13 (1.3%) missing values Missing
British has 71 (6.9%) missing values Missing
English native Language has 69 (6.7%) missing values Missing
Parent He attendance has 37 (3.6%) missing values Missing
Polar 4 Score has 118 (11.4%) missing values Missing
Care Leaver has 158 (15.3%) missing values Missing
Student Visa has 69 (6.7%) missing values Missing
UCAS Points has 54 (5.2%) missing values Missing
English has 160 (15.5%) missing values Missing
Maths has 161 (15.6%) missing values Missing
A Levels has 60 (5.8%) missing values Missing
Btec has 109 (10.6%) missing values Missing
Bursary has 162 (15.7%) missing values Missing
AWM year 2 has 110 (10.6%) missing values Missing
AWM year 3 has 269 (26.0%) missing values Missing
Second Sit has 208 (20.1%) zeros Zeros
Fails has 848 (82.1%) zeros Zeros
No Submissions has 423 (40.9%) zeros Zeros

Reproduction

Analysis started2022-08-11 18:24:59.253399
Analysis finished2022-08-11 18:25:46.354958
Duration47.1 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Course
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)1.3%
Missing2
Missing (%)0.2%
Memory size8.2 KiB
BA
389 
ba
380 
BA Business Management Enterpreneurship and Innovation
86 
BA Business Management
63 
Ba Business Management Finance
39 
Other values (8)
74 

Length

Max length55
Median length2
Mean length10.3986421
Min length2

Characters and Unicode

Total characters10721
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowBA Business Manangement Enterpreneurship and Innovation
2nd rowBA Business Management
3rd rowBA Business Management Enterpreneurship and Innovation
4th rowBA Business Management
5th rowBA Business Management Enterpreneurship and Innovation

Common Values

ValueCountFrequency (%)
BA389
37.7%
ba380
36.8%
BA Business Management Enterpreneurship and Innovation86
 
8.3%
BA Business Management63
 
6.1%
Ba Business Management Finance39
 
3.8%
BA Business Management Marketing37
 
3.6%
BA Business Management International Business12
 
1.2%
MBA10
 
1.0%
BA 6
 
0.6%
Ba4
 
0.4%
Other values (3)5
 
0.5%

Length

2022-08-11T19:25:46.530489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ba1020
54.2%
business253
 
13.5%
management238
 
12.7%
enterpreneurship89
 
4.7%
and89
 
4.7%
innovation89
 
4.7%
finance39
 
2.1%
marketing38
 
2.0%
international12
 
0.6%
mba11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n1424
13.3%
a1184
11.0%
e1091
10.2%
B904
 
8.4%
857
 
8.0%
s848
 
7.9%
A608
 
5.7%
i520
 
4.9%
t481
 
4.5%
b380
 
3.5%
Other values (18)2424
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7831
73.0%
Uppercase Letter2031
 
18.9%
Space Separator857
 
8.0%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1424
18.2%
a1184
15.1%
e1091
13.9%
s848
10.8%
i520
 
6.6%
t481
 
6.1%
b380
 
4.9%
u342
 
4.4%
r317
 
4.0%
g279
 
3.6%
Other values (9)965
12.3%
Uppercase Letter
ValueCountFrequency (%)
B904
44.5%
A608
29.9%
M290
 
14.3%
I101
 
5.0%
E89
 
4.4%
F39
 
1.9%
Space Separator
ValueCountFrequency (%)
857
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9862
92.0%
Common859
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1424
14.4%
a1184
12.0%
e1091
11.1%
B904
9.2%
s848
8.6%
A608
 
6.2%
i520
 
5.3%
t481
 
4.9%
b380
 
3.9%
u342
 
3.5%
Other values (15)2080
21.1%
Common
ValueCountFrequency (%)
857
99.8%
(1
 
0.1%
)1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1424
13.3%
a1184
11.0%
e1091
10.2%
B904
 
8.4%
857
 
8.0%
s848
 
7.9%
A608
 
5.7%
i520
 
4.9%
t481
 
4.5%
b380
 
3.5%
Other values (18)2424
22.6%

UCAS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
938 
False
95 
ValueCountFrequency (%)
True938
90.8%
False95
 
9.2%
2022-08-11T19:25:46.713004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

25 Above
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
no
868 
yes
161 
no
 
4

Length

Max length3
Median length2
Mean length2.159728945
Min length2

Characters and Unicode

Total characters2231
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no868
84.0%
yes161
 
15.6%
no 4
 
0.4%

Length

2022-08-11T19:25:46.843656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:47.001238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no872
84.4%
yes161
 
15.6%

Most occurring characters

ValueCountFrequency (%)
n872
39.1%
o872
39.1%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2227
99.8%
Space Separator4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n872
39.2%
o872
39.2%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2227
99.8%
Common4
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n872
39.2%
o872
39.2%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n872
39.1%
o872
39.1%
y161
 
7.2%
e161
 
7.2%
s161
 
7.2%
4
 
0.2%

Disability
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
967 
True
 
66
ValueCountFrequency (%)
False967
93.6%
True66
 
6.4%
2022-08-11T19:25:47.149350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Ethnicity
Categorical

MISSING

Distinct6
Distinct (%)0.6%
Missing13
Missing (%)1.3%
Memory size8.2 KiB
White
501 
Asian
279 
Black/Black British African
159 
Other ethnic background
76 
Other Black Background
 
3

Length

Max length27
Median length5
Mean length9.851960784
Min length5

Characters and Unicode

Total characters10049
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsian
2nd rowWhite
3rd rowAsian
4th rowWhite
5th rowAsian

Common Values

ValueCountFrequency (%)
White501
48.5%
Asian279
27.0%
Black/Black British African159
 
15.4%
Other ethnic background76
 
7.4%
Other Black Background3
 
0.3%
Mixed White and Asian2
 
0.2%
(Missing)13
 
1.3%

Length

2022-08-11T19:25:47.289974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:47.488445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
white503
33.5%
asian281
18.7%
black/black159
 
10.6%
british159
 
10.6%
african159
 
10.6%
other79
 
5.3%
background79
 
5.3%
ethnic76
 
5.1%
black3
 
0.2%
mixed2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i1339
13.3%
a842
 
8.4%
t817
 
8.1%
h817
 
8.1%
e660
 
6.6%
c635
 
6.3%
n597
 
5.9%
W503
 
5.0%
B483
 
4.8%
482
 
4.8%
Other values (15)2874
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7901
78.6%
Uppercase Letter1507
 
15.0%
Space Separator482
 
4.8%
Other Punctuation159
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1339
16.9%
a842
10.7%
t817
10.3%
h817
10.3%
e660
8.4%
c635
8.0%
n597
7.6%
r476
 
6.0%
s440
 
5.6%
k400
 
5.1%
Other values (8)878
11.1%
Uppercase Letter
ValueCountFrequency (%)
W503
33.4%
B483
32.1%
A440
29.2%
O79
 
5.2%
M2
 
0.1%
Space Separator
ValueCountFrequency (%)
482
100.0%
Other Punctuation
ValueCountFrequency (%)
/159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9408
93.6%
Common641
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1339
14.2%
a842
 
8.9%
t817
 
8.7%
h817
 
8.7%
e660
 
7.0%
c635
 
6.7%
n597
 
6.3%
W503
 
5.3%
B483
 
5.1%
r476
 
5.1%
Other values (13)2239
23.8%
Common
ValueCountFrequency (%)
482
75.2%
/159
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1339
13.3%
a842
 
8.4%
t817
 
8.1%
h817
 
8.1%
e660
 
6.6%
c635
 
6.3%
n597
 
5.9%
W503
 
5.0%
B483
 
4.8%
482
 
4.8%
Other values (15)2874
28.6%

Gender
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Male
636 
Female
391 
Female
 
3
Male
 
3

Length

Max length7
Median length4
Mean length4.768635044
Min length4

Characters and Unicode

Total characters4926
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male636
61.6%
Female391
37.9%
Female 3
 
0.3%
Male 3
 
0.3%

Length

2022-08-11T19:25:47.681928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:47.862445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male639
61.9%
female394
38.1%

Most occurring characters

ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3887
78.9%
Uppercase Letter1033
 
21.0%
Space Separator6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1427
36.7%
a1033
26.6%
l1033
26.6%
m394
 
10.1%
Uppercase Letter
ValueCountFrequency (%)
M639
61.9%
F394
38.1%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4920
99.9%
Common6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%
6
 
0.1%

desertion
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
874 
True
159 
ValueCountFrequency (%)
False874
84.6%
True159
 
15.4%
2022-08-11T19:25:48.018028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

British
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing71
Missing (%)6.9%
Memory size2.1 KiB
True
579 
False
383 
(Missing)
71 
ValueCountFrequency (%)
True579
56.1%
False383
37.1%
(Missing)71
 
6.9%
2022-08-11T19:25:48.155661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

English native Language
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing69
Missing (%)6.7%
Memory size2.1 KiB
True
497 
False
467 
(Missing)
69 
ValueCountFrequency (%)
True497
48.1%
False467
45.2%
(Missing)69
 
6.7%
2022-08-11T19:25:48.294291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Parent He attendance
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing37
Missing (%)3.6%
Memory size2.1 KiB
False
535 
True
461 
(Missing)
 
37
ValueCountFrequency (%)
False535
51.8%
True461
44.6%
(Missing)37
 
3.6%
2022-08-11T19:25:48.446882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Polar 4 Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.5%
Missing118
Missing (%)11.4%
Memory size8.2 KiB
4.0
314 
3.0
223 
5.0
180 
2.0
107 
1.0
91 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2745
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0314
30.4%
3.0223
21.6%
5.0180
17.4%
2.0107
 
10.4%
1.091
 
8.8%
(Missing)118
 
11.4%

Length

2022-08-11T19:25:48.579787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:48.780659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0314
34.3%
3.0223
24.4%
5.0180
19.7%
2.0107
 
11.7%
1.091
 
9.9%

Most occurring characters

ValueCountFrequency (%)
.915
33.3%
0915
33.3%
4314
 
11.4%
3223
 
8.1%
5180
 
6.6%
2107
 
3.9%
191
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1830
66.7%
Other Punctuation915
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0915
50.0%
4314
 
17.2%
3223
 
12.2%
5180
 
9.8%
2107
 
5.8%
191
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.915
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.915
33.3%
0915
33.3%
4314
 
11.4%
3223
 
8.1%
5180
 
6.6%
2107
 
3.9%
191
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.915
33.3%
0915
33.3%
4314
 
11.4%
3223
 
8.1%
5180
 
6.6%
2107
 
3.9%
191
 
3.3%

SLC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
yes
734 
no
287 
no
 
12

Length

Max length3
Median length3
Mean length2.722168441
Min length2

Characters and Unicode

Total characters2812
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes734
71.1%
no287
 
27.8%
no 12
 
1.2%

Length

2022-08-11T19:25:48.950540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:49.134429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes734
71.1%
no299
28.9%

Most occurring characters

ValueCountFrequency (%)
y734
26.1%
e734
26.1%
s734
26.1%
n299
10.6%
o299
10.6%
12
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2800
99.6%
Space Separator12
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y734
26.2%
e734
26.2%
s734
26.2%
n299
10.7%
o299
10.7%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2800
99.6%
Common12
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
y734
26.2%
e734
26.2%
s734
26.2%
n299
10.7%
o299
10.7%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y734
26.1%
e734
26.1%
s734
26.1%
n299
10.6%
o299
10.6%
12
 
0.4%

Care Leaver
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.5%
Missing158
Missing (%)15.3%
Memory size8.2 KiB
no
831 
no
 
24
yes
 
19
no
 
1

Length

Max length3
Median length2
Mean length2.050285714
Min length2

Characters and Unicode

Total characters1794
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no831
80.4%
no 24
 
2.3%
yes19
 
1.8%
no1
 
0.1%
(Missing)158
 
15.3%

Length

2022-08-11T19:25:49.290331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:49.474894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no856
97.8%
yes19
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n856
47.7%
o856
47.7%
25
 
1.4%
y19
 
1.1%
e19
 
1.1%
s19
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1769
98.6%
Space Separator25
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n856
48.4%
o856
48.4%
y19
 
1.1%
e19
 
1.1%
s19
 
1.1%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1769
98.6%
Common25
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n856
48.4%
o856
48.4%
y19
 
1.1%
e19
 
1.1%
s19
 
1.1%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n856
47.7%
o856
47.7%
25
 
1.4%
y19
 
1.1%
e19
 
1.1%
s19
 
1.1%

Student Visa
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.3%
Missing69
Missing (%)6.7%
Memory size8.2 KiB
no
792 
yes
155 
no
 
17

Length

Max length3
Median length2
Mean length2.178423237
Min length2

Characters and Unicode

Total characters2100
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no792
76.7%
yes155
 
15.0%
no 17
 
1.6%
(Missing)69
 
6.7%

Length

2022-08-11T19:25:49.641453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:49.813210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no809
83.9%
yes155
 
16.1%

Most occurring characters

ValueCountFrequency (%)
n809
38.5%
o809
38.5%
y155
 
7.4%
e155
 
7.4%
s155
 
7.4%
17
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2083
99.2%
Space Separator17
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n809
38.8%
o809
38.8%
y155
 
7.4%
e155
 
7.4%
s155
 
7.4%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2083
99.2%
Common17
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n809
38.8%
o809
38.8%
y155
 
7.4%
e155
 
7.4%
s155
 
7.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n809
38.5%
o809
38.5%
y155
 
7.4%
e155
 
7.4%
s155
 
7.4%
17
 
0.8%

Refugee
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.7%
Memory size2.1 KiB
False
1002 
True
 
24
(Missing)
 
7
ValueCountFrequency (%)
False1002
97.0%
True24
 
2.3%
(Missing)7
 
0.7%
2022-08-11T19:25:49.954831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

London Permanent Residence
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing5
Missing (%)0.5%
Memory size2.1 KiB
True
568 
False
460 
(Missing)
 
5
ValueCountFrequency (%)
True568
55.0%
False460
44.5%
(Missing)5
 
0.5%
2022-08-11T19:25:50.095456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

UCAS Points
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)6.1%
Missing54
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean109.113381
Minimum72
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:50.277968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile82
Q196
median104
Q3120
95-th percentile152
Maximum168
Range96
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.20580245
Coefficient of variation (CV)0.1851817098
Kurtosis0.8416214006
Mean109.113381
Median Absolute Deviation (MAD)11
Skewness0.9814397744
Sum106822
Variance408.2744527
MonotonicityNot monotonic
2022-08-11T19:25:50.487408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9684
 
8.1%
10451
 
4.9%
12847
 
4.5%
8036
 
3.5%
12036
 
3.5%
11235
 
3.4%
8435
 
3.4%
8833
 
3.2%
10033
 
3.2%
10330
 
2.9%
Other values (50)559
54.1%
(Missing)54
 
5.2%
ValueCountFrequency (%)
724
 
0.4%
8036
3.5%
8222
2.1%
8435
3.4%
851
 
0.1%
8610
 
1.0%
875
 
0.5%
8833
3.2%
897
 
0.7%
906
 
0.6%
ValueCountFrequency (%)
16825
2.4%
1625
 
0.5%
1608
 
0.8%
1551
 
0.1%
1538
 
0.8%
15215
1.5%
1486
 
0.6%
1464
 
0.4%
14418
1.7%
1366
 
0.6%

English
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.9%
Missing160
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean4.924398625
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:50.836985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.385131692
Coefficient of variation (CV)0.2812793596
Kurtosis0.3617214903
Mean4.924398625
Median Absolute Deviation (MAD)1
Skewness0.7507784704
Sum4299
Variance1.918589804
MonotonicityNot monotonic
2022-08-11T19:25:50.967636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4290
28.1%
5256
24.8%
6120
11.6%
382
 
7.9%
853
 
5.1%
751
 
4.9%
211
 
1.1%
910
 
1.0%
(Missing)160
15.5%
ValueCountFrequency (%)
211
 
1.1%
382
 
7.9%
4290
28.1%
5256
24.8%
6120
11.6%
751
 
4.9%
853
 
5.1%
910
 
1.0%
ValueCountFrequency (%)
910
 
1.0%
853
 
5.1%
751
 
4.9%
6120
11.6%
5256
24.8%
4290
28.1%
382
 
7.9%
211
 
1.1%

Maths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.9%
Missing161
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean4.774082569
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:51.139176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q35
95-th percentile7
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.19916459
Coefficient of variation (CV)0.2511822057
Kurtosis0.761532845
Mean4.774082569
Median Absolute Deviation (MAD)1
Skewness0.569859444
Sum4163
Variance1.437995713
MonotonicityNot monotonic
2022-08-11T19:25:51.285784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4345
33.4%
5256
24.8%
6124
 
12.0%
760
 
5.8%
346
 
4.5%
222
 
2.1%
814
 
1.4%
95
 
0.5%
(Missing)161
15.6%
ValueCountFrequency (%)
222
 
2.1%
346
 
4.5%
4345
33.4%
5256
24.8%
6124
 
12.0%
760
 
5.8%
814
 
1.4%
95
 
0.5%
ValueCountFrequency (%)
95
 
0.5%
814
 
1.4%
760
 
5.8%
6124
 
12.0%
5256
24.8%
4345
33.4%
346
 
4.5%
222
 
2.1%

A Levels
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing60
Missing (%)5.8%
Memory size2.1 KiB
True
519 
False
454 
(Missing)
60 
ValueCountFrequency (%)
True519
50.2%
False454
43.9%
(Missing)60
 
5.8%
2022-08-11T19:25:51.460318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Btec
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing109
Missing (%)10.6%
Memory size2.1 KiB
False
545 
True
379 
(Missing)
109 
ValueCountFrequency (%)
False545
52.8%
True379
36.7%
(Missing)109
 
10.6%
2022-08-11T19:25:51.594958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing3
Missing (%)0.3%
Memory size2.1 KiB
True
538 
False
492 
(Missing)
 
3
ValueCountFrequency (%)
True538
52.1%
False492
47.6%
(Missing)3
 
0.3%
2022-08-11T19:25:51.727604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Bursary
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing162
Missing (%)15.7%
Memory size2.1 KiB
False
626 
True
245 
(Missing)
162 
ValueCountFrequency (%)
False626
60.6%
True245
 
23.7%
(Missing)162
 
15.7%
2022-08-11T19:25:51.875210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Attendance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)6.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean75.07751938
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:52.050258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile46
Q164
median76
Q388
95-th percentile97
Maximum100
Range80
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.7430199
Coefficient of variation (CV)0.2096901979
Kurtosis-0.6289405519
Mean75.07751938
Median Absolute Deviation (MAD)12
Skewness-0.395967175
Sum77480
Variance247.8426755
MonotonicityNot monotonic
2022-08-11T19:25:52.271667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6034
 
3.3%
9231
 
3.0%
9529
 
2.8%
7428
 
2.7%
9627
 
2.6%
8127
 
2.6%
9027
 
2.6%
7226
 
2.5%
8825
 
2.4%
6525
 
2.4%
Other values (53)753
72.9%
ValueCountFrequency (%)
201
 
0.1%
251
 
0.1%
406
0.6%
416
0.6%
4214
1.4%
433
 
0.3%
448
0.8%
4512
1.2%
467
0.7%
4710
1.0%
ValueCountFrequency (%)
10015
1.5%
9915
1.5%
9820
1.9%
9715
1.5%
9627
2.6%
9529
2.8%
9425
2.4%
9313
1.3%
9231
3.0%
9116
1.5%

AWM year 1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)5.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.71414729
Minimum30
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:52.498061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q146
median58
Q371
95-th percentile82
Maximum85
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.24820418
Coefficient of variation (CV)0.2426707164
Kurtosis-1.117789841
Mean58.71414729
Median Absolute Deviation (MAD)12
Skewness0.1441683938
Sum60593
Variance203.0113225
MonotonicityNot monotonic
2022-08-11T19:25:52.691545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4138
 
3.7%
4434
 
3.3%
4334
 
3.3%
4534
 
3.3%
4731
 
3.0%
4629
 
2.8%
8029
 
2.8%
4228
 
2.7%
4027
 
2.6%
5026
 
2.5%
Other values (46)722
69.9%
ValueCountFrequency (%)
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
355
0.5%
366
0.6%
372
 
0.2%
386
0.6%
396
0.6%
ValueCountFrequency (%)
8519
1.8%
8417
1.6%
8312
1.2%
8213
1.3%
818
 
0.8%
8029
2.8%
7917
1.6%
7813
1.3%
7724
2.3%
7620
1.9%

AWM year 2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)6.3%
Missing110
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean60.36078007
Minimum0
Maximum87
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:52.910958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q148
median60
Q374
95-th percentile83
Maximum87
Range87
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.50092224
Coefficient of variation (CV)0.256804538
Kurtosis-0.7390363903
Mean60.36078007
Median Absolute Deviation (MAD)13
Skewness-0.1851098572
Sum55713
Variance240.2785903
MonotonicityNot monotonic
2022-08-11T19:25:53.110933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7927
 
2.6%
4027
 
2.6%
5525
 
2.4%
7125
 
2.4%
4923
 
2.2%
8123
 
2.2%
6222
 
2.1%
4622
 
2.1%
8322
 
2.1%
5821
 
2.0%
Other values (48)686
66.4%
(Missing)110
 
10.6%
ValueCountFrequency (%)
02
 
0.2%
303
 
0.3%
316
0.6%
3211
1.1%
339
0.9%
346
0.6%
3514
1.4%
367
0.7%
375
 
0.5%
3812
1.2%
ValueCountFrequency (%)
8712
1.2%
8515
1.5%
8414
1.4%
8322
2.1%
8218
1.7%
8123
2.2%
8016
1.5%
7927
2.6%
7819
1.8%
7718
1.7%

AWM year 3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct57
Distinct (%)7.5%
Missing269
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean58.45418848
Minimum0
Maximum85
Zeros7
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:53.329349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q145
median58
Q371.25
95-th percentile82
Maximum85
Range85
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation15.73808383
Coefficient of variation (CV)0.269237915
Kurtosis0.1792101405
Mean58.45418848
Median Absolute Deviation (MAD)13
Skewness-0.3377345833
Sum44659
Variance247.6872826
MonotonicityNot monotonic
2022-08-11T19:25:53.530369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4325
 
2.4%
4124
 
2.3%
4423
 
2.2%
5222
 
2.1%
6320
 
1.9%
4520
 
1.9%
7619
 
1.8%
6619
 
1.8%
6519
 
1.8%
4219
 
1.8%
Other values (47)554
53.6%
(Missing)269
26.0%
ValueCountFrequency (%)
07
0.7%
302
 
0.2%
316
0.6%
322
 
0.2%
335
0.5%
345
0.5%
357
0.7%
3610
1.0%
3712
1.2%
384
 
0.4%
ValueCountFrequency (%)
8513
1.3%
8415
1.5%
8310
1.0%
8217
1.6%
816
 
0.6%
8017
1.6%
7913
1.3%
7812
1.2%
7712
1.2%
7619
1.8%

Overall AWM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct166
Distinct (%)16.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.11660207
Minimum20.5
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:53.736816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.5
5-th percentile38
Q151
median59.33333333
Q366.33333333
95-th percentile74.66666667
Maximum84
Range63.5
Interquartile range (IQR)15.33333333

Descriptive statistics

Standard deviation11.2569827
Coefficient of variation (CV)0.1936965049
Kurtosis-0.2948603498
Mean58.11660207
Median Absolute Deviation (MAD)7.333333333
Skewness-0.4223969715
Sum59976.33333
Variance126.7196594
MonotonicityNot monotonic
2022-08-11T19:25:53.937281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3333333324
 
2.3%
58.3333333319
 
1.8%
67.3333333317
 
1.6%
6017
 
1.6%
6716
 
1.5%
6416
 
1.5%
60.6666666716
 
1.5%
5715
 
1.5%
6115
 
1.5%
56.3333333315
 
1.5%
Other values (156)862
83.4%
ValueCountFrequency (%)
20.51
 
0.1%
27.333333331
 
0.1%
27.666666671
 
0.1%
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
356
0.6%
368
0.8%
ValueCountFrequency (%)
841
 
0.1%
82.666666671
 
0.1%
822
0.2%
812
0.2%
80.666666671
 
0.1%
80.51
 
0.1%
80.333333332
0.2%
803
0.3%
793
0.3%
78.51
 
0.1%

Progress
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size2.1 KiB
True
848 
False
184 
(Missing)
 
1
ValueCountFrequency (%)
True848
82.1%
False184
 
17.8%
(Missing)1
 
0.1%
2022-08-11T19:25:54.131760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

First Sit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.016472868
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:54.256428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.303847906
Coefficient of variation (CV)0.3246250998
Kurtosis-0.7057159947
Mean4.016472868
Median Absolute Deviation (MAD)1
Skewness0.02471814333
Sum4145
Variance1.700019361
MonotonicityNot monotonic
2022-08-11T19:25:54.390070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3373
36.1%
4218
21.1%
6189
18.3%
5183
17.7%
236
 
3.5%
133
 
3.2%
(Missing)1
 
0.1%
ValueCountFrequency (%)
133
 
3.2%
236
 
3.5%
3373
36.1%
4218
21.1%
5183
17.7%
6189
18.3%
ValueCountFrequency (%)
6189
18.3%
5183
17.7%
4218
21.1%
3373
36.1%
236
 
3.5%
133
 
3.2%

Second Sit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing8
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.834146341
Minimum0
Maximum5
Zeros208
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:54.526706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.251485398
Coefficient of variation (CV)0.6823258154
Kurtosis-0.8062111839
Mean1.834146341
Median Absolute Deviation (MAD)1
Skewness-0.01364452657
Sum1880
Variance1.566215701
MonotonicityNot monotonic
2022-08-11T19:25:54.663847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3349
33.8%
2231
22.4%
0208
20.1%
1199
19.3%
520
 
1.9%
418
 
1.7%
(Missing)8
 
0.8%
ValueCountFrequency (%)
0208
20.1%
1199
19.3%
2231
22.4%
3349
33.8%
418
 
1.7%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
418
 
1.7%
3349
33.8%
2231
22.4%
1199
19.3%
0208
20.1%

Fails
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.5639534884
Minimum0
Maximum5
Zeros848
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:54.792116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.309437159
Coefficient of variation (CV)2.321888571
Kurtosis3.595023888
Mean0.5639534884
Median Absolute Deviation (MAD)0
Skewness2.20925508
Sum582
Variance1.714625674
MonotonicityNot monotonic
2022-08-11T19:25:54.929748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0848
82.1%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
110
 
1.0%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0848
82.1%
110
 
1.0%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
ValueCountFrequency (%)
532
 
3.1%
439
 
3.8%
350
 
4.8%
253
 
5.1%
110
 
1.0%
0848
82.1%

No Submissions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.234496124
Minimum0
Maximum5
Zeros423
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:55.230454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.364384136
Coefficient of variation (CV)1.105215407
Kurtosis-0.06991856543
Mean1.234496124
Median Absolute Deviation (MAD)1
Skewness0.9483452924
Sum1274
Variance1.861544072
MonotonicityNot monotonic
2022-08-11T19:25:55.367088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
476
 
7.4%
396
 
9.3%
2165
 
16.0%
1252
24.4%
0423
40.9%

Late Submission
Categorical

Distinct4
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size8.2 KiB
1.0
423 
0.0
409 
2.0
175 
3.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3096
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0423
40.9%
0.0409
39.6%
2.0175
16.9%
3.025
 
2.4%
(Missing)1
 
0.1%

Length

2022-08-11T19:25:55.521675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T19:25:55.677259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0423
41.0%
0.0409
39.6%
2.0175
17.0%
3.025
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2064
66.7%
Other Punctuation1032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01441
69.8%
1423
 
20.5%
2175
 
8.5%
325
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.1032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Pass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean91.62080103
Minimum16.66666667
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-11T19:25:55.808908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.66666667
5-th percentile33.33333333
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range83.33333333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.83237346
Coefficient of variation (CV)0.216461472
Kurtosis3.835663087
Mean91.62080103
Median Absolute Deviation (MAD)0
Skewness-2.272267351
Sum94552.66667
Variance393.3230371
MonotonicityNot monotonic
2022-08-11T19:25:55.945542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
100848
82.1%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
16.6666666710
 
1.0%
861
 
0.1%
(Missing)1
 
0.1%
ValueCountFrequency (%)
16.6666666710
 
1.0%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
861
 
0.1%
100848
82.1%
ValueCountFrequency (%)
100848
82.1%
861
 
0.1%
83.3333333332
 
3.1%
66.6666666739
 
3.8%
5050
 
4.8%
33.3333333352
 
5.0%
16.6666666710
 
1.0%

Re Takes
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size2.1 KiB
False
878 
True
154 
(Missing)
 
1
ValueCountFrequency (%)
False878
85.0%
True154
 
14.9%
(Missing)1
 
0.1%
2022-08-11T19:25:56.112097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2022-08-11T19:25:40.103754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:08.910319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.635737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:14.172982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:16.716888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:19.556021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.049372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.464935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:27.090002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:29.954937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:32.852718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:35.273508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:37.503080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:40.283274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:09.263401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.810781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:14.357176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:16.914360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:19.744518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.224902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.621029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:27.295453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:30.174350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:33.050070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:35.446047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:37.773358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:40.460801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:09.485317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.999787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:14.551656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:17.134770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:19.938998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.399436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.820645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:27.489933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:30.395269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:33.247542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:35.632548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:37.955870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:40.637327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:09.682789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:12.202246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:14.760609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:17.354183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:20.150433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.569981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.998177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:27.872911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:30.586757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:33.464471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:35.818053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:38.154849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:40.814857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:09.879264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:12.401712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:14.994983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:17.577097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:20.400763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.767453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:25.239446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:28.169971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:30.788218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:33.670918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.000564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:38.343345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:40.981412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:10.075739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:12.578241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:15.204422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:17.783544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:20.590246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:22.938993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:25.477810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:28.368441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:30.972725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:33.854428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.179599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:38.508902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.155946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:10.252775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:12.752774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:15.416854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:17.959077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:20.773268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:23.111044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:25.633394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:28.581871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:31.222059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.016993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.349147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:38.880908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.325494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:10.419329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:12.914347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:15.584408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:18.162532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:20.936829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:23.302531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.014376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:28.783843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:31.440475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.195515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.502245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.041987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.490052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:10.611815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:13.113813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:15.771905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:18.368979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:21.137293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:23.502996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.199880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:28.979322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:31.719239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.368054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.670306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.216786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.651621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:10.852171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:13.455899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:15.950428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:18.558982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:21.326787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:23.702463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.372423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:29.201727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:31.926685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.564538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.833869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.388158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.804214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.051639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:13.636416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:16.140428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:18.758448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:21.506307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:23.882980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.548450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:29.392219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:32.110703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.744058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:36.999427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.554713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:41.969770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.284359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:13.811949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:16.327929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:18.963903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:21.689817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.076464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.734952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:29.585702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:32.296206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:34.918945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:37.168973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.739727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:42.133333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:11.469099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:13.987478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:16.521410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:19.171537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:21.866349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:24.261967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:26.902503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:29.787392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:32.478719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:35.089001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:37.338521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T19:25:39.912266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-11T19:25:56.254717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-11T19:25:56.584833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-11T19:25:56.960829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-11T19:25:57.364749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-11T19:25:57.839480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-11T19:25:42.493932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-11T19:25:44.130086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-11T19:25:45.165826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-11T19:25:46.009881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CourseUCAS25 AboveDisabilityEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudent VisaRefugeeLondon Permanent ResidenceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceAWM year 1AWM year 2AWM year 3Overall AWMProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPassRe Takes
0BA Business Manangement Enterpreneurship and InnovationnononoAsianMalenononoyes4.0nonoyesnoyes98.05.04.0yesnoyesno86.085.058.043.062.000000yes3.03.00.02.02.0100.000000yes
1BA Business ManagementnononoWhiteMaleyesnonoyes2.0yesnononono101.05.05.0yesnoyesyes55.040.032.0NaN36.000000no1.02.05.03.00.083.333333no
2BA Business Management Enterpreneurship and InnovationnononoAsianMaleyesyesyesyes4.0yesnononoyes129.04.04.0yesnoyesno57.041.0NaNNaN41.000000yes6.00.00.00.00.0100.000000no
3BA Business ManagementnoyesnoWhiteFemaleyesnonono3.0yesnononoyes110.09.08.0yesnoyesno48.041.043.0NaN42.000000yes6.00.00.00.00.0100.000000no
4BA Business Management Enterpreneurship and InnovationnononoAsianMalenoyesyesyes4.0yesnononoyes130.06.05.0yesnoyesno83.055.049.059.054.333333yes4.02.00.02.00.0100.000000no
5BA Business Management Enterpreneurship and InnovationyesnonoAsianMalenoyesyesyes3.0yesnononoyes112.06.04.0noyesnono71.046.046.043.045.000000yes3.03.00.00.01.0100.000000no
6BA Business Management MarketingyesnonoWhiteMalenonoyesno5.0nonoyesnono89.06.05.0yesnonono96.078.070.079.075.666667yes4.02.00.00.02.0100.000000no
7BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno4.0yesnononoyes103.04.05.0yesnonono67.043.085.061.063.000000yes3.03.00.03.00.0100.000000no
8BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno2.0nonononoyes128.04.04.0noyesnoyes89.076.058.044.059.333333yes6.00.00.00.00.0100.000000no
9BA Business ManagementyesnonoWhiteFemalenoyesyesnoNaNnonononono91.04.04.0nononono92.049.083.067.066.333333yes6.00.00.01.01.0100.000000no

Last rows

CourseUCAS25 AboveDisabilityEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudent VisaRefugeeLondon Permanent ResidenceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceAWM year 1AWM year 2AWM year 3Overall AWMProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPassRe Takes
1023BAyesnonoAsianMalenononoyes5.0yesnononono107.06.07.0noyesnono96.080.0NaNNaN80.0yes6.00.00.00.01.0100.000000no
1024BAyesyesnoWhiteMalenoyesNaNno3.0yesnononono103.05.06.0noyesyesno67.040.0NaNNaN40.0yes1.05.00.03.00.0100.000000no
1025BAyesnonoNaNMalenoyesNaNyes4.0yesNaNnonono100.05.04.0yesnoyesno70.053.0NaNNaN53.0yes6.00.00.00.01.0100.000000no
1026BAyesyesnoNaNFemalenononoyes3.0yesNaNnonono113.03.06.0noyesnono64.073.0NaNNaN73.0yes3.03.00.02.01.0100.000000no
1027BAyesyesnoNaNMalenoyesnoyes2.0yesNaNnonoyes118.05.05.0yesnoyesyes96.068.0NaNNaN68.0yes3.03.00.01.00.0100.000000no
1028BAyesnonoOther ethnic backgroundFemaleyesnoyesno5.0yesNaNnonono102.04.04.0yesnoyesno55.045.0NaNNaN45.0yes6.00.00.00.01.0100.000000no
1029BAyesnonoNaNMalenoyesyesyes4.0yesnononoyes109.04.04.0yesnoyesno66.077.0NaNNaN77.0yes6.00.00.00.00.0100.000000no
1030BAnoyesnoAsianFemaleyesnonono3.0nonononono104.06.05.0yesnoyesno42.033.0NaNNaN33.0no1.01.02.04.01.033.333333yes
1031BAnoyesnoOther ethnic backgroundMalenononoyes4.0yesnononono101.06.06.0noyesnono60.076.0NaNNaN76.0yes6.00.00.00.00.0100.000000no
1032BAnoyesnoOther ethnic backgroundFemalenonononoNaNnonoyesnono104.08.04.0nononono71.080.0NaNNaN80.0yes6.00.00.00.00.0100.000000no